Top 30 DynamoDB Interview Questions and Answers for 2025

Preparing for a DynamoDB interview? Whether you’re a developer, database administrator, or cloud architect, this comprehensive guide covers the most commonly asked questions about Amazon DynamoDB in technical interviews. From basic concepts to advanced features, these questions and answers will help you showcase your knowledge of AWS’s popular NoSQL database service.

Table of Contents

1. Basic DynamoDB Concepts
2. Data Modeling and Design
3. Performance and Scaling
4. Advanced Features
5. Security and Monitoring
6. Best Practices and Common Patterns

Basic DynamoDB Concepts

1. What is Amazon DynamoDB?

Answer: Amazon DynamoDB is a fully managed NoSQL database service provided by AWS that delivers fast and predictable performance with seamless scalability. It’s a key-value and document database that can handle any amount of data and traffic. DynamoDB offloads the administrative burdens of operating and scaling a distributed database so that developers don’t have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling.

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2. What are the key features of DynamoDB?

Answer: The key features of DynamoDB include:

• Fully managed service with automatic scaling
• Multi-region, multi-master architecture with global tables
• Built-in security, backup and restore capabilities, and in-memory caching
• ACID transaction support
• Event-driven programming with DynamoDB Streams
• Serverless design with on-demand capacity mode
• Point-in-time recovery (continuous backups)
• Time To Live (TTL) for automatic data expiration
• Support for both document and key-value data structures
• Single-digit millisecond response times at any scale

3. How is DynamoDB different from traditional relational databases?

Answer: Key differences between DynamoDB and relational databases include:

Feature	DynamoDB	Relational Databases
Data Model	NoSQL (schemaless)	SQL (schema-based)
Scaling	Horizontal scaling (add partitions)	Typically vertical scaling (larger servers)
Query Flexibility	Limited to key-based access patterns	Flexible queries with complex joins
Consistency	Offers both eventual and strong consistency	Typically strong consistency
Transactions	Supports transactions, but with limitations	Full ACID transaction support
Schema	Flexible schema, can vary between items	Rigid schema enforced for all rows
Indexes	Primary key and limited secondary indexes	Multiple indexes for various query patterns
Management	Fully managed by AWS	Often requires manual management

4. What is eventual consistency and strong consistency in DynamoDB?

Answer: In DynamoDB:

• Eventually Consistent Reads: Might not reflect the results of a recently completed write operation. Data is usually consistent within a second. This option provides higher read throughput.

• Strongly Consistent Reads: Returns a response with the most up-to-date data that reflects updates from all prior successful write operations. This consistency comes at the cost of higher latency and reduced read throughput.

By default, DynamoDB performs eventually consistent reads. You need to explicitly request strongly consistent reads when needed.

5. What data types does DynamoDB support?

Answer: DynamoDB supports the following data types:

1. Scalar Types:

   • String
   • Number
   • Binary
   • Boolean
   • Null

2. Document Types:

   • List (ordered collection of values)
   • Map (unordered collection of name-value pairs)

3. Set Types:

   • String Set
   • Number Set
   • Binary Set

6. What is a DynamoDB item and how large can it be?

Answer: An item in DynamoDB is a collection of attributes, similar to a row in a traditional database. Each item has a primary key that uniquely identifies it within a table. The maximum size of a DynamoDB item is 400KB, including both attribute names and values. This limit is important to consider when designing your data model, especially when storing large documents or binary data.

Data Modeling and Design

7. What is a partition key and sort key in DynamoDB?

Answer: In DynamoDB:

• Partition Key (Hash Key): Determines the partition where an item is stored. DynamoDB uses the partition key’s value as input to an internal hash function to determine storage location. It must be unique for tables with simple primary keys.

• Sort Key (Range Key): Optional in a composite primary key. It’s used to sort items with the same partition key. Multiple items can share the same partition key but must have different sort keys.

Together, the partition key and sort key form a table’s primary key. The partition key is used for data distribution, while the sort key enables range queries within a partition.

8. What are the best practices for choosing a partition key?

Answer: Best practices for choosing a partition key include:

• Choose a key with high cardinality (many distinct values)
• Select attributes that will be used in the most common and important queries
• Avoid keys that lead to “hot partitions” (disproportionate traffic to one partition)
• Consider using composite attributes if a single attribute doesn’t distribute well
• Avoid monotonically increasing/decreasing values (like timestamps) as they can lead to uneven distribution
• If access patterns require it, consider random suffixes or prefixes to spread load
• Ensure the partition key is included in all primary access patterns

9. What is a secondary index in DynamoDB and what types are available?

Answer: A secondary index in DynamoDB allows you to query a table using an alternative key, in addition to queries against the primary key. DynamoDB supports two types of secondary indexes:

• Global Secondary Index (GSI): An index with a partition key and optional sort key that can be different from those on the base table. GSIs span all partitions of the base table, can be created or deleted at any time, and have their own provisioned throughput settings.

• Local Secondary Index (LSI): An index that has the same partition key as the base table but a different sort key. LSIs are “local” to each partition key value. LSIs must be created when the table is created and cannot be added later.

Each table can have up to 20 GSIs and 5 LSIs.

10. What is the single-table design pattern in DynamoDB and why is it used?

Answer: Single-table design is a pattern where multiple entity types are stored in a single DynamoDB table instead of creating separate tables for each entity. This pattern is used because:

• It reduces the number of queries needed to retrieve related data (no joins in DynamoDB)
• It allows complex hierarchical data relationships to be represented efficiently
• It can improve performance by reducing the number of round trips to the database
• It helps manage throughput costs by consolidating capacity
• It simplifies transaction operations that need to operate across related items

The pattern works by using a combination of partition key, sort key, and attribute design that allows different entity types to coexist while maintaining query efficiency. For example, using prefixes in keys like “USER#123” and “ORDER#456” to distinguish between entity types.

11. How would you model a one-to-many relationship in DynamoDB?

Answer: There are several ways to model one-to-many relationships in DynamoDB:

1. Denormalization (Embedding): Store the “many” side items directly within the “one” side item as a nested attribute. For example, storing a user’s orders as an array within the user item.

2. Adjacency List Pattern: Use the same partition key for both the “one” and “many” sides, with different sort keys. For example:

   • PK=“USER#123”, SK=“PROFILE” (user details)
   • PK=“USER#123”, SK=“ORDER#1” (first order)
   • PK=“USER#123”, SK=“ORDER#2” (second order)

3. Inverted Index with GSI: Use a Global Secondary Index to provide access from the “many” side back to the “one” side. For example, your base table might have OrderID as the partition key, and a GSI might have UserID as the partition key.

The choice depends on access patterns, data size, and query requirements.

Performance and Scaling

12. What are capacity units in DynamoDB?

Answer: Capacity units are the units of throughput in DynamoDB:

• Read Capacity Units (RCU): One RCU represents one strongly consistent read per second, or two eventually consistent reads per second, for items up to 4KB in size. For items larger than 4KB, more RCUs are required.

• Write Capacity Units (WCU): One WCU represents one write per second for items up to 1KB in size. For items larger than 1KB, more WCUs are required.

DynamoDB offers two capacity modes:

• Provisioned capacity: You specify the number of RCUs and WCUs your application needs.
• On-demand capacity: DynamoDB automatically scales up and down based on your actual traffic, and you pay per request.

13. What is Auto Scaling in DynamoDB and how does it work?

Answer: DynamoDB Auto Scaling is a feature that automatically adjusts provisioned throughput capacity in response to actual traffic patterns. Here’s how it works:

1. You set target utilization (typically 70%)
2. You define minimum and maximum capacity units
3. AWS Application Auto Scaling monitors your table’s consumed capacity using CloudWatch metrics
4. When traffic exceeds target utilization for a sustained period, capacity is increased
5. When traffic falls below target for a sustained period, capacity is decreased
6. Changes occur within boundaries set by min/max capacity
7. There’s a cooldown period between scaling activities (default is 5 minutes)

Auto Scaling helps balance cost and performance by automatically adjusting capacity based on actual usage.

14. What happens when you exceed your provisioned throughput in DynamoDB?

Answer: When you exceed your provisioned throughput in DynamoDB:

1. Requests above your capacity are throttled (rejected with a ProvisionedThroughputExceededException)
2. The AWS SDK automatically retries throttled requests with exponential backoff
3. DynamoDB returns CloudWatch metrics indicating throttled requests
4. For tables with Auto Scaling enabled, capacity might increase (though not immediately)

To handle throttling, applications should:

• Implement retry with exponential backoff
• Consider using on-demand capacity mode for unpredictable workloads
• Implement caching strategies to reduce reads
• Consider more efficient data models to reduce required capacity
• Monitor CloudWatch metrics to identify and address throttling early

15. What is adaptive capacity in DynamoDB?

Answer: Adaptive capacity is a DynamoDB feature that automatically handles uneven access patterns across partitions. Even when data and access are unevenly distributed (creating “hot” partitions), adaptive capacity automatically redistributes throughput capacity to handle these hot spots, reducing the chance of throttling on specific partitions.

Key aspects of adaptive capacity:

• Works automatically without configuration
• Responds to traffic patterns in real-time
• Isolates frequently accessed items
• Doesn’t eliminate the need for a good partition key design
• Available in both provisioned and on-demand capacity modes

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16. How do you optimize costs in DynamoDB?

Answer: To optimize costs in DynamoDB:

1. Choose the right capacity mode:

   • Use on-demand for unpredictable or sporadic workloads
   • Use provisioned with Auto Scaling for predictable workloads

2. Optimize read operations:

   • Use eventually consistent reads when possible (half the cost of strongly consistent reads)
   • Implement caching with DAX or application-level caching
   • Consider using Scan with page size limits to reduce consumed capacity

3. Optimize write operations:

   • Batch writes where possible to reduce API calls
   • Use TTL to automatically remove expired data
   • Consider compressing large attribute values

4. Data modeling improvements:

   • Use sparse indexes to reduce index size
   • Project only necessary attributes to secondary indexes
   • Consider item size (smaller items = lower cost per operation)

5. Monitoring and management:

   • Use CloudWatch to identify underutilized tables
   • Schedule scaling for predictable traffic patterns
   • Reserve capacity for steady-state workloads with reserved capacity

Advanced Features

17. What is DynamoDB Streams and how does it work?

Answer: DynamoDB Streams is a feature that captures a time-ordered sequence of item-level modifications in any DynamoDB table and stores this information in a log for up to 24 hours. It’s essentially a change data capture (CDC) system for DynamoDB.

Key aspects of DynamoDB Streams:

• Each stream record appears exactly once in the stream
• For each item that is modified, the stream records appear in the same sequence as the actual modifications
• Stream records can contain the “before” and “after” images of modified items
• Streams can be consumed by Lambda functions, Kinesis Data Streams client, or the DynamoDB Streams Kinesis Adapter
• Common use cases include replication, triggers, analytics integration, and maintaining derived views

18. What is DynamoDB Accelerator (DAX) and when would you use it?

Answer: DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement (microsecond latency for cached reads and queries).

You would use DAX in these scenarios:

• Applications requiring the fastest possible response times (microseconds)
• Read-intensive workloads where you want to reduce DynamoDB costs
• Repeated reads against the same items
• Applications that are read-heavy but need strongly consistent reads for certain operations (DAX handles this mixed workload)

DAX is best for read-heavy applications that are sensitive to latency. It’s not ideal for:

• Write-heavy applications (writes still go directly to DynamoDB)
• Applications that rarely read the same data
• Applications that can tolerate higher read latency

19. What are DynamoDB Transactions and what are their limitations?

Answer: DynamoDB Transactions provide atomicity, consistency, isolation, and durability (ACID) across multiple items within and across tables. They allow you to group multiple actions together and submit them as a single all-or-nothing operation.

Key characteristics:

• Can include up to 100 items or 4MB of data (whichever is smaller)
• Support both reads and writes in the same transaction
• Can span multiple tables within the same AWS account and region

Limitations:

• Only available within a single region (not across global tables)
• Consume twice the WCUs/RCUs of standard operations
• Cannot be performed on items in the same table with the same primary key
• No partial success - entire transaction succeeds or fails
• Cannot include operations on tables with TTL enabled and TTL-deleted items
• Not supported for tables with DynamoDB Streams enabled
• Higher latency than non-transactional operations

20. What are DynamoDB Global Tables and how do they work?

Answer: DynamoDB Global Tables provide a fully managed, multi-region, multi-master database solution for globally distributed applications. They allow automatic replication across your choice of AWS regions with eventual consistency.

Key aspects of Global Tables:

• Active-active replication (multi-master) - read and write in any region
• Built-in conflict resolution using “last writer wins” semantics
• Automatic propagation of changes between regions within seconds
• Table structure, indexes, and data remain synchronized across regions
• Provides regional failover capability for disaster recovery
• Improves latency by bringing data closer to users in different geographic locations

To set up Global Tables, you must:

1. Create identical tables in multiple regions
2. Enable DynamoDB Streams on all tables
3. Have enough write capacity in all regions to handle replication traffic

21. What is Time to Live (TTL) in DynamoDB?

Answer: Time to Live (TTL) is a feature that allows you to define when items in a DynamoDB table expire so that they can be automatically deleted from the database without consuming write throughput.

Key aspects:

• You define a specific attribute name that DynamoDB looks for when determining if an item is eligible for expiration
• The TTL attribute value should be a timestamp (epoch time in seconds)
• DynamoDB automatically deletes expired items within 48 hours of expiration
• Deletion uses background processes, so it doesn’t consume write capacity
• TTL deletions are reflected in DynamoDB Streams if enabled
• TTL is useful for removing sensitive data, session data, event logs, or temporary data

Security and Monitoring

22. How do you secure data in DynamoDB?

Answer: To secure data in DynamoDB:

1. Encryption:

   • All DynamoDB tables are encrypted at rest by default
   • Choose between AWS owned keys, AWS managed keys, or customer managed keys (CMKs)

2. Authentication and Authorization:

   • Use IAM policies to control access at table and item level
   • Implement attribute-based access control for fine-grained permissions
   • Use IAM roles for applications and services
   • Consider VPC endpoints to keep traffic within AWS network

3. Network Security:

   • Access DynamoDB via VPC endpoints to keep traffic within AWS
   • Implement network ACLs and security groups for access control

4. Audit and Compliance:

   • Enable AWS CloudTrail to log API calls
   • Monitor with CloudWatch for suspicious activity
   • Consider DynamoDB Streams for change tracking

5. Application-level Security:

   • Implement client-side encryption for sensitive fields
   • Follow the principle of least privilege in application roles

23. How do you monitor a DynamoDB table?

Answer: To monitor DynamoDB:

1. CloudWatch Metrics: DynamoDB automatically publishes metrics to CloudWatch, including:

   • Consumed read/write capacity units
   • Throttled requests
   • Latency statistics
   • System errors and user errors
   • Successful request count

2. CloudWatch Alarms: Set up alarms for:

   • High consumption approaching provisioned limits
   • Throttled requests
   • High latency
   • Error rates

3. CloudTrail: For auditing API calls to DynamoDB

4. Contributor Insights: Identify the most accessed and throttled keys

5. X-Ray: For tracing and analyzing requests through your application to DynamoDB

6. Table metrics in AWS Console: Visual representation of table performance and utilization

7. CloudWatch Dashboard: Create custom dashboards combining various DynamoDB metrics

Common monitoring best practices include setting alarms for throttling events, monitoring consumed vs. provisioned capacity, and tracking error rates and latency.

24. What is fine-grained access control in DynamoDB?

Answer: Fine-grained access control in DynamoDB allows you to restrict access to specific items and attributes within a table. It’s implemented through:

1. IAM Policies with Conditions: You can create policies that use condition expressions to limit access based on:

   • The primary key values of items
   • Specific attributes within items
   • The values of those attributes

2. Identity-based vs. Resource-based Policies:

   • Identity-based: Attached to IAM entities (users, groups, roles)
   • Resource-based: Attached directly to DynamoDB resources

Example use cases:

• Allow users to access only items they own (where userId matches their identity)
• Restrict access to sensitive attributes like PII
• Implement row-level or cell-level security in multi-tenant applications
• Allow read-only access to certain attributes while permitting full access to others

Example policy condition:

{
  "Condition": {
    "ForAllValues:StringEquals": {
      "dynamodb:LeadingKeys": ["${cognito-identity.amazonaws.com:sub}"]
    }
  }
}

This condition would restrict a user to only access items where their identity matches the partition key.

Best Practices and Common Patterns

25. What are the best practices for DynamoDB data modeling?

Answer: Best practices for DynamoDB data modeling include:

1. Understand access patterns first:

   • Identify all query patterns before designing
   • Optimize for the most frequent and performance-critical access patterns

2. Choose effective keys:

   • Select partition keys with high cardinality and even distribution
   • Use sort keys to create hierarchical relationships
   • Consider composite keys (combining values) for unique constraints

3. Use single-table design when appropriate:

   • Consolidate multiple entity types in one table when they’re related
   • Use prefixes or type indicators in keys (e.g., “USER#123”, “ORDER#456”)

4. Optimize for query efficiency:

   • Denormalize data to avoid multiple queries
   • Duplicate data as needed to support multiple access patterns
   • Use sparse indexes for queries on subsets of items

5. Manage item size:

   • Keep items small when possible
   • For large items, consider storing bulky attributes in S3 and references in DynamoDB

6. Design for scaling:

   • Avoid hot partitions by distributing workload
   • Use GSIs to handle alternative access patterns
   • Consider future growth when selecting partition keys

7. Use appropriate secondary indexes:

   • Create only necessary indexes (each adds write costs)
   • Project only required attributes to indexes
   • Use GSIs for flexibility, LSIs for strong consistency needs

26. What is the difference between Query and Scan operations in DynamoDB?

Answer: Key differences between Query and Scan operations:

Feature	Query	Scan
Functionality	Finds items based on primary key values	Examines every item in a table
Performance	Efficient, uses indexes	Less efficient, full table scan
Filtering	Primary filtering via key conditions, secondary via FilterExpression	Only uses FilterExpression
Consistency	Can be strongly or eventually consistent	Can be strongly or eventually consistent
Result Order	Results sorted by sort key (ascending by default)	Results in random order
Capacity Consumption	Reads only items matching key condition	Reads entire table
Best For	Finding specific items by key	Data analytics, infrequent operations
Parallel Operation	Limited parallel query (via key range)	Supports parallel scan (segments)

Best practices:

• Always prefer Query over Scan when possible
• Use appropriate indexes to enable Query operations
• When using Scan, use pagination to limit impact
• Consider parallel scan for large tables when necessary

27. How would you handle a hot partition problem in DynamoDB?

Answer: To handle a hot partition problem in DynamoDB:

1. Write sharding/distribution:

   • Add a random suffix to partition keys (e.g., userId_1, userId_2)
   • Use calculated suffixes based on write volume
   • Implement application-level logic to write and read across shards

2. Change the partition key:

   • Redesign the data model with a better-distributed partition key
   • Consider composite partition keys that spread load

3. Leverage adaptive capacity:

   • Modern DynamoDB has adaptive capacity that helps with some hot partition issues
   • Still requires monitoring and may not solve severe cases

4. Caching strategies:

   • Implement DAX or application-level caching
   • Cache commonly accessed hot items

5. Isolate high-volume entities:

   • Move frequently accessed items to separate tables

6. For time-series data:

   • Use time periods in partition keys (e.g., “2023-07”)
   • Rotate tables for time-series data

7. Use on-demand capacity mode:

   • Switches to per-request billing and helps with unpredictable workloads

8. Monitor and detect:

   • Set up CloudWatch metrics to identify throttling
   • Use Contributor Insights to find hot keys

28. What are some common DynamoDB design patterns?

Answer: Common DynamoDB design patterns include:

1. Single-Table Design:

   • Store multiple entity types in one table
   • Use prefixes or discriminators in keys
   • Optimize for complex queries with one request

2. Adjacency List Pattern:

   • Model hierarchical relationships (one-to-many, many-to-many)
   • Use same partition key for related items, different sort keys
   • Efficiently query all related items in one operation

3. Sparse Index Pattern:

   • Only items with the indexed attribute appear in the index
   • Create selective indexes for specific queries
   • Reduce index size and cost

4. Composite Key Pattern:

   • Combine multiple attributes into keys
   • Enable complex queries without additional indexes
   • Support multiple access patterns with the same structure

5. GSI Write Sharding:

   • Distribute writes across GSI partitions
   • Mitigate hot partition problems
   • Use random or calculated suffixes in GSI partition key

6. GSI Overloading:

   • Use the same GSI for multiple access patterns
   • Differentiate by attribute or prefix in the key
   • Reduce the number of indexes needed

7. Sort Key for Version Control:

   • Use timestamps or version numbers in sort keys
   • Query latest version or historical versions
   • Implement optimistic concurrency control

8. Time-Series Data Pattern:

   • Use time periods as partition keys
   • Consider table-per-period for very high volume
   • Balance between query efficiency and management overhead

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29. How would you implement pagination in DynamoDB?

Answer: To implement pagination in DynamoDB:

1. Using Query or Scan with Limit:
   • Set a Limit parameter to specify the maximum number of items per page
   • Use the ExclusiveStartKey parameter for subsequent requests
   • The LastEvaluatedKey in the response becomes the ExclusiveStartKey for the next page

Example code flow (pseudocode):

// First page request
response = dynamodb.query({
  TableName: "MyTable",
  KeyConditionExpression: "partitionKey = :pk",
  Limit: 10
});

// Display items
displayItems(response.Items);

// If there are more items
if (response.LastEvaluatedKey) {
  // Store for next page request
  nextPageKey = response.LastEvaluatedKey;
}

// When user requests next page
nextPageResponse = dynamodb.query({
  TableName: "MyTable",
  KeyConditionExpression: "partitionKey = :pk",
  Limit: 10,
  ExclusiveStartKey: nextPageKey
});

2. Best Practices:

   • Store LastEvaluatedKey in your application state or pass to the client (securely)
   • Consider using consistent page sizes (though the last page may be smaller)
   • Implement timeout handling if a pagination token expires
   • For UI applications, consider “infinite scroll” patterns to simplify user experience

3. Challenges:

   • The result set may change between page requests (new items or deleted items)
   • LastEvaluatedKey is opaque and should be treated as a token, not parsed
   • Tokens can’t be used across different operations (Query vs Scan)

30. What are the most common DynamoDB errors and how would you handle them?

Answer: Common DynamoDB errors and their handling strategies:

1. ProvisionedThroughputExceededException:

   • Caused by: Exceeding provisioned capacity
   • Handling: Implement exponential backoff and retry, increase capacity, use Auto Scaling, consider on-demand capacity mode, or review data access patterns

2. ResourceNotFoundException:

   • Caused by: Accessing a non-existent table
   • Handling: Verify table names, check region settings, ensure tables are created before access

3. ConditionalCheckFailedException:

   • Caused by: Failed conditional write operation
   • Handling: Implement proper conflict resolution logic, retry with updated conditions if appropriate

4. ValidationException:

   • Caused by: Invalid parameter values or request structure
   • Handling: Validate input parameters, check attribute types and formats

5. ItemCollectionSizeLimitExceededException:

   • Caused by: Exceeding 10GB limit for items with same partition key
   • Handling: Redesign data model, distribute items across more partition keys

6. ThrottlingException (for API calls):

   • Caused by: Too many control plane operations (CreateTable, UpdateTable)
   • Handling: Implement throttling limits on management operations, retry with backoff

7. LimitExceededException:

   • Caused by: Exceeding service limits (tables per account, etc.)
   • Handling: Request limit increases, optimize resource usage

8. TransactionCanceledException:

   • Caused by: Failed transaction due to conflict or conditions
   • Handling: Analyze CancellationReasons in response, implement appropriate retry strategy

Best practices for error handling:

• Implement proper retry with exponential backoff for retriable errors
• Log detailed error information for troubleshooting
• Set up CloudWatch alarms for recurring errors
• Use the AWS SDK’s built-in retry mechanisms
• Have fallback strategies for critical operations

Conclusion

Mastering these DynamoDB interview questions and answers will help you demonstrate both your theoretical understanding and practical knowledge of AWS’s powerful NoSQL database service. Remember that interviewers are not just looking for memorized answers, but for your ability to apply these concepts to real-world scenarios.

When answering DynamoDB questions in an interview, try to incorporate your personal experience using the service, discuss specific challenges you’ve faced, and explain how you’ve applied best practices to solve real problems.

Good luck with your interview!

Related Resources

• What is DynamoDB?
• DynamoDB Tutorial for Beginners
• DynamoDB Best Practices
• DynamoDB Data Modeling Techniques
• DynamoDB Pricing and Free Tier